Diabetic Foot Ulcer Detection Using Deep Learning: A Focus on EfficientNet for Enhanced Accuracy

Diabetic foot ulcers is a common side effect of diabetes.A DFU is a critical condition that can lead to the amputation of a diabetic patient’s lower limb.Early diagnosis thus becomes very critical.The occurrence of diabetes complications can be linked to a lack of information among diabetes patients, who are often unaware of the negative consequences of their illness.In light of this, the study suggests using the EfficientNet deep neural network model for diabetic foot ulcer prediction.Around 500 photos of healthy and the diabetic ulcer-affected patients’ feet are used to train the EfficientNet model. The depth, breadth, and picture resolution levels are the three distinct characteristics on which the network processing takes place.By combining the input image’s depth, width, and resolution and using fewer parameters, EfficientNet has outperformed earlier methods. The EfficientNet model outperformed well-known models including AlexNet and VGG. It gave maximum testing acuraccy of 100%.These result suggest that EfficientNet is a reliable tool for early DFU detection, potentially reducing the risk of amputations in diabetic patients.